English Français العربية

gutsy

Official registry AI and agents

Local gut check before risky agent actions: calibrated safe/intended probabilities, no API calls.

Source code Documentation

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

PYPI package gutsy-mcp
Claude Code 
claude mcp add gutsy -e 'GUTSY_URL=http://127.0.0.1:8765' -e 'GUTSY_INFERENCE_DIR=<GUTSY_INFERENCE_DIR>' -e 'GUTSY_PROCEED_MIN=0.85' -e 'GUTSY_BLOCK_MIN=0.50' -- uvx gutsy-mcp==0.1.2
JSON configuration (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "gutsy": {
            "command": "uvx",
            "args": [
                "gutsy-mcp==0.1.2"
            ],
            "env": {
                "GUTSY_URL": "http://127.0.0.1:8765",
                "GUTSY_INFERENCE_DIR": "<GUTSY_INFERENCE_DIR>",
                "GUTSY_PROCEED_MIN": "0.85",
                "GUTSY_BLOCK_MIN": "0.50"
            }
        }
    }
}

Environment variables

  • GUTSY_URL URL of the local gutsy-inference server
  • GUTSY_INFERENCE_DIR Directory with models.json; if set, gutsy-mcp starts gutsy-inference itself
  • GUTSY_PROCEED_MIN Minimum P(proceed) and P(intended) to recommend proceeding
  • GUTSY_BLOCK_MIN Minimum P(block) to recommend blocking

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. gutsy-mcp
  2. What it is and isn't
  3. 1. Start the gutsy runtime
  4. 2. Add the MCP server
  5. 3. Optional: enforce it with a Claude Code hook
  6. Tool

gutsy-mcp

A local gut check for coding agents. Before Claude Code, Codex, Cursor or Copilot runs rm -rf, force-pushes, deploys or sends something, it calls gut_check and gets back calibrated probabilities that the action is intended and safe, plus a recommendation: proceed, confirm (ask the user first) or block.

Runs on your CPU with gutsy (0.8B, 775 MB GGUF). No API calls, no per-call cost, deterministic, and your code never leaves the machine.

What it is and isn't

It's a fast second opinion that adds friction where it's warranted. It is not a security boundary: the agent decides whether to call it and writes the context it sees, and a 0.8B model is weak at ambiguity and multi-step rules (see the model card). Keep your normal permission settings on. Default thresholds aren't tuned for your workflow; log the probabilities and adjust them.

1. Start the gutsy runtime

git clone https://github.com/kouhxp/gutsy
pip install -e gutsy/gutsy-inference
hf download kouhxp/gutsy --include "gutsy-0.8b-v04*" --local-dir gutsy/gutsy-inference/models
cd gutsy/gutsy-inference && cp models.example.json models.json
gutsy-inference check && gutsy-inference serve     # http://127.0.0.1:8765

Or set GUTSY_INFERENCE_DIR=/path/to/gutsy/gutsy-inference and gutsy-mcp starts it on first use.

2. Add the MCP server

Claude Code:

claude mcp add gutsy -- uvx gutsy-mcp

Cursor (.cursor/mcp.json) / Claude Desktop:

{ "mcpServers": { "gutsy": { "command": "uvx", "args": ["gutsy-mcp"] } } }

VS Code (.vscode/mcp.json):

{ "servers": { "gutsy": { "type": "stdio", "command": "uvx", "args": ["gutsy-mcp"] } } }

Codex (~/.codex/config.toml):

[mcp_servers.gutsy]
command = "uvx"
args = ["gutsy-mcp"]

Then tell the agent when to use it (CLAUDE.md, AGENTS.md, .cursor/rules):

Before deleting files, rewriting git history, force-pushing, touching databases, deploying, publishing or sending anything, call gut_check. If it doesn't return proceed, stop and ask me.

3. Optional: enforce it with a Claude Code hook

An MCP tool only runs if the agent remembers to call it. The hook runs on every risky-looking Bash command regardless. It can only ask or deny, never auto-approve.

uv tool install gutsy-mcp      # puts gutsy-hook on PATH

.claude/settings.json:

{
  "hooks": {
    "PreToolUse": [
      { "matcher": "Bash", "hooks": [{ "type": "command", "command": "gutsy-hook", "timeout": 60 }] }
    ]
  }
}

block becomes a confirmation prompt by default; set GUTSY_HOOK_ALLOW_DENY=1 to deny outright.

Tool

gut_check(action, context="", user_request="") returns:

{
  "recommendation": "confirm",
  "reason": "Not confident enough to proceed without the user.",
  "p_intended": 0.41,
  "p_reversible": 0.08,
  "verdict_probabilities": {"proceed": 0.22, "confirm": 0.61, "block": 0.17},
  "reject": 0.04,
  "confidence": 0.42
}
env vardefault
GUTSY_URLhttp://127.0.0.1:8765runtime URL
GUTSY_INFERENCE_DIRlets gutsy-mcp start the runtime
GUTSY_MODELruntime defaultmodel name from models.json
GUTSY_PROCEED_MIN0.85
GUTSY_BLOCK_MIN0.50
GUTSY_API_KEYif you run serve --api-key-env

License: Apache 2.0.

Frequently asked questions

What is gutsy?

Local gut check before risky agent actions: calibrated safe/intended probabilities, no API calls.

How do I install gutsy?

It runs on your machine. In Claude Code: claude mcp add gutsy -e 'GUTSY_URL=http://127.0.0.1:8765' -e 'GUTSY_INFERENCE_DIR=<GUTSY_INFERENCE_DIR>' -e 'GUTSY_PROCEED_MIN=0.85' -e 'GUTSY_BLOCK_MIN=0.50' -- uvx gutsy-mcp==0.1.2. For other clients, paste the JSON configuration from this page into their MCP settings.

Does gutsy need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is gutsy open source?

Yes. Its code is on GitHub under the Apache-2.0 license.

Who maintains gutsy?

It is published by kouhxp. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.